tf.stop_gradient

tf.stop_gradient(
input,
name=None
)

Defined in generated file: tensorflow/python/ops/gen_array_ops.py.

Stops gradient computation.

When executed in a graph, this op outputs its input tensor as-is.

When building ops to compute gradients, this op prevents the contribution of
its inputs to be taken into account. Normally, the gradient generator adds ops
to a graph to compute the derivatives of a specified 'loss' by recursively
finding out inputs that contributed to its computation. If you insert this op
in the graph it inputs are masked from the gradient generator. They are not
taken into account for computing gradients.

This is useful any time you want to compute a value with TensorFlow but need
to pretend that the value was a constant. Some examples include:

The EM algorithm where the M-step should not involve backpropagation
through the output of the E-step.

Contrastive divergence training of Boltzmann machines where, when
differentiating the energy function, the training must not backpropagate
through the graph that generated the samples from the model.

Adversarial training, where no backprop should happen through the adversarial
example generation process.